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Question 46:
A company wants to deploy a highly available, fault-tolerant web application that automatically scales with traffic. They need the ability to handle an entire Availability Zone failure without downtime. Which AWS architecture is most appropriate?
A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer
B) Single EC2 instance in one Availability Zone with manual scaling
C) EC2 instances in one Availability Zone behind a Network Load Balancer
D) Amazon Lightsail instance with snapshots for scaling
Answer:
A) Auto Scaling group across multiple Availability Zones behind an Application Load Balancer
Explanation:
The company in this scenario requires a highly available, fault-tolerant web application that can automatically scale to handle varying traffic loads and survive the failure of an entire Availability Zone. Achieving these objectives requires a combination of redundancy, scalability, and intelligent traffic distribution. The most appropriate solution is deploying an Auto Scaling group across multiple Availability Zones (AZs) behind an Application Load Balancer (ALB). This architecture ensures both high availability and fault tolerance while allowing automatic scaling in response to traffic demands.
An Auto Scaling group (ASG) is a core AWS feature that automatically adjusts the number of EC2 instances in response to changes in traffic or other predefined metrics. This ensures that the application can scale out during periods of high demand and scale in when traffic decreases, optimizing cost and performance. By deploying the ASG across multiple Availability Zones, the architecture provides redundancy in case one AZ becomes unavailable. AWS Availability Zones are physically separate locations within a region, each with independent power, networking, and connectivity. If an entire AZ fails, the ASG can launch instances in the remaining healthy AZs, ensuring the application continues to operate without downtime.
The Application Load Balancer (ALB) plays a critical role in distributing incoming traffic across all healthy instances in multiple AZs. ALB supports advanced routing capabilities, SSL termination, and health checks. Health checks monitor the status of each EC2 instance, ensuring that traffic is only routed to healthy targets. If an instance becomes unhealthy or an AZ fails, the ALB automatically redirects traffic to the remaining healthy instances. This combination of ALB and ASG ensures that the web application can withstand failures at the instance or AZ level while providing consistent performance to users.
Option B, a single EC2 instance in one Availability Zone with manual scaling, does not meet the requirements for high availability or fault tolerance. If the instance or the entire AZ fails, the application will experience downtime. Manual scaling requires human intervention to add or remove instances based on traffic, which is inefficient and unable to respond dynamically to sudden traffic spikes. This approach also creates a single point of failure, making it unsuitable for critical applications.
Option C, EC2 instances in one Availability Zone behind a Network Load Balancer (NLB), offers load balancing but lacks multi-AZ fault tolerance. NLBs are optimized for TCP traffic and high-performance network-level routing but do not inherently provide automatic scaling. Deploying all instances in a single AZ still exposes the application to downtime if that AZ fails. While NLB is excellent for certain use cases like low-latency TCP connections, it does not address the requirement for handling AZ failures or automatic scaling for HTTP/S web traffic.
Option D, an Amazon Lightsail instance with snapshots for scaling, is designed for simpler workloads and small-scale deployments. Lightsail can be cost-effective for straightforward applications, but it does not support the same level of automation, high availability, or multi-AZ deployment as EC2 with Auto Scaling and ALB. Relying on snapshots for scaling is a manual process and cannot provide immediate recovery from an AZ failure or dynamic traffic handling.
By combining an Auto Scaling group with multiple Availability Zones and an Application Load Balancer, the architecture achieves several critical goals. It ensures high availability by distributing instances across multiple AZs, provides fault tolerance by automatically replacing failed instances or redirecting traffic, and supports automatic scaling to respond to varying traffic levels. Additionally, using ALB enables advanced routing, SSL offloading, and seamless integration with AWS security services such as AWS WAF and AWS Shield for protection against web attacks and DDoS threats.
This architecture maximizes uptime, minimizes operational complexity, and provides a scalable, resilient foundation for a web application. Deploying an Auto Scaling group across multiple Availability Zones behind an Application Load Balancer is the most effective and reliable approach to meet the company’s requirements
Question 47:
A company wants to migrate an active PostgreSQL database to AWS with minimal downtime. Continuous writes occur, and the database must stay synchronized until the final cutover. Which service is most suitable?
A) AWS Database Migration Service (DMS)
B) AWS DataSync
C) AWS Snowball Edge
D) AWS Glue
Answer:
A) AWS Database Migration Service (DMS)
Explanation:
Migrating a live database with ongoing transactions requires a solution capable of performing a full data load followed by continuous replication. AWS DMS captures changes in real time using Change Data Capture (CDC), keeping the target database synchronized with the source.
The migration process begins with a full data load of existing tables, followed by CDC that replicates inserts, updates, and deletes. Applications continue writing to the source database, and cutover is only needed after synchronization is confirmed.
Option B, AWS DataSync, handles file-level transfers, not live database replication. Option C, Snowball Edge, is for bulk offline migration and cannot maintain real-time sync. Option D, Glue, is for ETL and data transformation, not transactional replication.
AWS DMS is fully managed, supports heterogeneous migrations, and aligns with SAA-C03 best practices for minimizing downtime during database migration.
Question 48:
A company wants to analyze large volumes of semi-structured log data stored in Amazon S3 using SQL without building ETL pipelines. Which AWS service meets this requirement?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue
Answer:
A) Amazon Athena
Explanation:
Amazon Athena is a serverless, interactive query service that enables analysts and developers to query large datasets stored in Amazon S3 using standard SQL without the need to provision or manage any servers. It is particularly suited for semi-structured data formats such as JSON, Parquet, Avro, or ORC, which are common in log files, IoT data, or application telemetry. Athena allows users to perform ad-hoc analysis directly on raw data in S3 without the need to move the data into a database or data warehouse.
One of the primary advantages of Athena is that it eliminates the need for ETL pipelines to prepare or transform data before analysis. Traditionally, to query semi-structured data in a relational database, you would need to design an ETL workflow to extract the data from raw storage, transform it into a structured format, and then load it into a database like Redshift. Athena bypasses this by using a schema-on-read approach: the structure of the data is applied at query time rather than at load time. This allows teams to get insights quickly without building complex ETL processes, saving both time and operational effort.
Athena is highly scalable and can handle large volumes of data because queries are executed in parallel across multiple nodes managed entirely by AWS. It also integrates with AWS Glue Data Catalog, enabling the management and discovery of datasets in S3. This integration allows users to define metadata once and then query the datasets repeatedly using SQL without needing to redefine the schema each time.
Option B, Amazon EMR, is a managed Hadoop and Spark platform designed for distributed data processing. While EMR can process large datasets and support SQL through engines like Hive or Presto, it requires provisioning clusters and managing them, which introduces operational overhead. For ad-hoc querying of S3 data without building ETL pipelines, EMR is not the most efficient solution because it is better suited for batch processing or complex data transformations rather than serverless, interactive SQL queries.
Option C, Amazon Redshift, is a fully managed data warehouse optimized for structured, relational data and analytical queries. Redshift can handle large-scale queries efficiently, but it requires loading the data into tables, often requiring ETL pipelines to convert semi-structured logs into a structured format. This extra step contradicts the requirement of analyzing data without building ETL pipelines.
Option D, AWS Glue, is primarily a serverless ETL service designed for extracting, transforming, and loading data. While Glue can transform semi-structured data and catalog datasets, it does not provide direct SQL querying capabilities and requires workflows to process data, which again does not meet the requirement of ad-hoc SQL analysis without ETL.
Athena also provides cost efficiency because you are billed only for the amount of data scanned by queries. Optimizations such as storing logs in columnar formats (e.g., Parquet or ORC) and partitioning datasets can significantly reduce query costs while improving performance. Queries can also join multiple datasets stored in S3, enabling complex analysis without the need for pre-processing.
From a security perspective, Athena integrates with AWS Identity and Access Management (IAM) to control access to datasets and supports encryption for data in transit (HTTPS) and at rest (S3 server-side encryption). Auditability is enhanced through integration with AWS CloudTrail, allowing administrators to track who ran which queries and when.
In SAA-C03 exam scenarios, understanding Athena’s serverless SQL querying capabilities, schema-on-read model, and direct integration with S3 and Glue Data Catalog is critical. Many exam questions focus on choosing Athena for use cases that involve analyzing raw or semi-structured data without the overhead of building ETL pipelines, while differentiating it from EMR, Redshift, or Glue.
Amazon Athena meets all requirements for analyzing semi-structured logs stored in S3: it provides serverless, ad-hoc SQL queries, requires no ETL pipelines, supports scalable and parallel processing, integrates with AWS Glue Data Catalog, and ensures security and cost efficiency. It is the most appropriate solution for organizations seeking rapid insights from raw S3 datasets without operational complexity.
Question 49:
A company wants to store sensitive API keys and database credentials for multiple microservices on ECS Fargate. Each service must access only its authorized secrets with automatic rotation and encryption. Which service should be used?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS
Answer:
A) AWS Secrets Manager
Explanation:
The company in this scenario is running multiple microservices on ECS Fargate and needs to securely store sensitive information such as API keys and database credentials. The requirements include ensuring that each microservice only accesses its authorized secrets, providing automatic rotation of secrets, and maintaining strong encryption. Among the options provided, AWS Secrets Manager is the best-suited service to meet all these requirements.
AWS Secrets Manager is a fully managed service specifically designed to store, manage, and retrieve secrets in a secure and scalable manner. It provides a centralized location for sensitive data such as database credentials, API keys, OAuth tokens, and other configuration secrets. One of the key advantages of Secrets Manager is its ability to integrate with AWS Identity and Access Management (IAM), allowing fine-grained access control. This means that each microservice can be assigned an IAM role that grants permission to access only its own authorized secrets. By enforcing the principle of least privilege, Secrets Manager ensures that no service can inadvertently or maliciously access secrets intended for other services.
Automatic rotation is another critical feature of AWS Secrets Manager. Secrets can be configured to rotate automatically according to a defined schedule, for example, every 30 days or whenever security policies require it. This ensures that credentials such as database passwords or API keys are regularly updated without manual intervention, reducing the risk of compromised secrets being used for extended periods. When integrated with services like Amazon RDS or other supported databases, Secrets Manager can automatically update the credentials in both the database and the microservices consuming them, ensuring a seamless and secure update process. This feature not only improves security but also reduces operational overhead.
Encryption is a fundamental aspect of AWS Secrets Manager. All secrets are encrypted at rest using AWS Key Management Service (KMS), which ensures that sensitive information remains secure even if the underlying storage is compromised. Secrets are also transmitted securely over HTTPS when accessed, providing encryption in transit. This combination of encryption at rest and in transit ensures that sensitive credentials are protected throughout their lifecycle.
Option B, Amazon RDS Parameter Groups, is not suitable for this scenario because parameter groups are specifically designed to manage configuration settings for RDS database instances. They are not intended for storing arbitrary secrets for multiple microservices, nor do they support automatic rotation or fine-grained per-service access control. Using RDS parameter groups would not meet the security or operational requirements for ECS microservices.
Option C, EC2 Instance Metadata, provides temporary credentials and instance-specific information to EC2 instances. While this feature can provide role-based access to AWS services for EC2 instances, it is not designed for securely storing or managing secrets across multiple microservices. ECS Fargate tasks do not rely on EC2 instance metadata in the same way, making this approach impractical for the use case.
Option D, Amazon EFS, is a shared filesystem that can store files accessible by multiple ECS tasks. While EFS can store configuration files, it lacks the features required for secure secrets management, including automatic rotation, per-service access control, and built-in encryption for secrets. Using EFS would require additional layers of security and operational effort, increasing complexity and potential for misconfiguration.
By choosing AWS Secrets Manager, the company can centralize secret management for multiple microservices, enforce strict access control, and automatically rotate credentials while keeping all secrets encrypted. ECS Fargate tasks can retrieve secrets securely through IAM roles assigned to the tasks, ensuring that each microservice only accesses its authorized secrets. This reduces operational complexity, enhances security, and ensures compliance with best practices for handling sensitive credentials in a microservices architecture.
Question 50:
A company needs to ingest high-volume IoT telemetry data in real time. Multiple applications must consume the same data simultaneously, ensuring durability and scalability. Which AWS service is most appropriate?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ
Answer:
A) Amazon Kinesis Data Streams
Explanation:
Amazon Kinesis Data Streams is designed for high-throughput real-time data ingestion. It partitions data into shards for parallel consumption by multiple applications. Each shard has defined read/write capacity, and additional shards can be added to scale throughput linearly.
Enhanced fan-out allows multiple consumers to read the same stream independently without impacting each other’s performance. Data is replicated across multiple AZs for durability. Integration with AWS Lambda enables serverless, event-driven processing.
Option B, SQS, does not provide parallel consumption for multiple high-throughput applications. Option C, SNS, does not retain messages for replay and is not optimized for real-time analytics at high volume. Option D, Amazon MQ, is designed for traditional messaging workloads and cannot handle millions of messages per second efficiently.
Kinesis Data Streams meets all requirements for real-time ingestion, durability, and scalability, making it the recommended solution for IoT telemetry and streaming data workloads in SAA-C03 scenarios.
Question 51:
A company wants to deploy a stateless web application on Amazon EC2 instances behind an Application Load Balancer. They need session data to persist across instance terminations and auto-scaling events. Which solution is most appropriate?
A) Use Amazon ElastiCache for Redis to store session data
B) Store session data locally on EC2 instances
C) Store session data in Amazon S3
D) Enable sticky sessions on the Application Load Balancer
Answer:
A) Use Amazon ElastiCache for Redis to store session data
Explanation:
In horizontally scaled web applications, storing session data locally on EC2 instances is unreliable because instances can terminate during auto-scaling or fail unexpectedly, leading to lost sessions. Amazon ElastiCache for Redis provides a centralized, in-memory session store accessible by all instances in the Auto Scaling group.
Redis is optimized for low latency and high throughput, making it ideal for session management. It supports replication and clustering, ensuring high availability and fault tolerance. Using Redis allows multiple EC2 instances to read and update session data consistently, which is critical for applications that must scale seamlessly.
Option B stores sessions locally, which is unreliable and breaks stateless architecture best practices. Option C, S3, is durable but introduces latency for frequent read/write operations, making it unsuitable for session data. Option D, sticky sessions, binds users to a specific EC2 instance, reducing the benefits of auto scaling and failing if the instance terminates.
ElastiCache aligns with AWS Well-Architected Framework principles, providing performance, fault tolerance, and scalability, which are core concepts tested in SAA-C03.
Question 52:
A company needs to migrate a live MySQL database to AWS with minimal downtime. Continuous writes occur, and the database must remain synchronized until cutover. Which AWS service should be used?
A) AWS Database Migration Service (DMS)
B) AWS DataSync
C) AWS Snowball Edge
D) AWS Glue
Answer:
A) AWS Database Migration Service (DMS)
Explanation:
AWS Database Migration Service (DMS) is specifically designed to migrate databases to AWS with minimal downtime, even when the source database is actively being updated. This makes it the ideal choice for live, transactional databases like MySQL where continuous writes occur. DMS supports homogeneous migrations (MySQL to MySQL, PostgreSQL to PostgreSQL, etc.) as well as heterogeneous migrations (for example, Oracle to Amazon Aurora), providing flexibility depending on the target database platform.
One of the key features of DMS is change data capture (CDC). CDC continuously monitors the source database for updates, inserts, and deletes, and applies these changes to the target database in near real-time. This ensures that the source and target remain synchronized throughout the migration process, which is crucial for minimizing downtime and maintaining application availability. The replication process is designed to handle large volumes of changes efficiently, so the system remains responsive and consistent even under heavy load.
DMS is fully managed, which reduces the operational complexity of migration. AWS handles tasks such as provisioning replication instances, monitoring replication, and automatically retrying failed operations. Additionally, it provides monitoring through Amazon CloudWatch and detailed migration metrics, allowing teams to track performance, latency, and replication health throughout the process.
Option B, AWS DataSync, is intended primarily for transferring files between on-premises storage and AWS storage services like Amazon S3 or Amazon EFS. While DataSync is efficient for bulk file transfers, it is not designed for transactional database migration or for handling continuous database writes.
Option C, AWS Snowball Edge, is a physical device used for large-scale data transfers when network connectivity is limited. It can move terabytes to petabytes of data, but it is not suitable for live database migration with continuous writes because it involves offline data transfer rather than near real-time synchronization.
Option D, AWS Glue, is a serverless data integration service mainly used for ETL (extract, transform, load) workflows and analytics preparation. Glue is not designed for live database replication or minimal downtime migration, as it typically operates in batch mode rather than continuous real-time synchronization.
Using DMS also aligns with AWS best practices for migration. By leveraging a managed replication service, companies can avoid manually scripting complex replication or failover processes. DMS also integrates well with Amazon RDS and Amazon Aurora, which are common target databases in AWS migration scenarios. Before cutover, the source and target databases remain in sync, and once the final replication is verified, cutover can occur with minimal service interruption, sometimes in just a few minutes, depending on database size and complexity.
In SAA-C03 exam scenarios, understanding the distinction between DMS, DataSync, Snowball, and Glue is critical. The exam often presents situations involving minimal downtime, live database synchronization, and continuous data updates. The correct choice is DMS because it explicitly provides managed, near real-time database replication, supports both homogeneous and heterogeneous migrations, and ensures operational continuity during migration.
AWS Database Migration Service is the recommended solution for live MySQL database migration with minimal downtime because it handles continuous writes, keeps the source and target synchronized through change data capture, reduces operational complexity with a fully managed service, and provides monitoring and reliability features that meet enterprise-grade migration requirements.
Question 53:
A company wants to analyze large volumes of log data in Amazon S3 using SQL without creating ETL pipelines. Which AWS service should be used?
A) Amazon Athena
B) Amazon EMR
C) Amazon Redshift
D) AWS Glue
Answer:
A) Amazon Athena
Explanation:
In this scenario, the company wants to analyze large volumes of log data stored in Amazon S3 using SQL without creating traditional ETL pipelines. The key requirements are direct querying of data, simplicity, and avoiding the overhead of complex data processing workflows. Amazon Athena is the most suitable AWS service for this use case.
Amazon Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. Because it is serverless, there is no need to provision or manage infrastructure, which simplifies the workflow significantly. Users can immediately start querying data in S3 by defining a schema for the data and using SQL queries, making it ideal for ad hoc analysis of logs, JSON, CSV, Parquet, ORC, or other supported formats. Athena is designed for scenarios where quick insights are needed from large datasets without the need to load the data into a separate analytics platform.
One of the key benefits of Athena is that it works directly with data in S3, eliminating the need for ETL pipelines to move or transform the data. Traditionally, data analysis might require extracting data from S3, transforming it into a structured format, and loading it into a data warehouse. Athena removes this overhead by enabling schema-on-read, which means the structure of the data is interpreted at query time. This approach allows companies to quickly analyze logs or other raw datasets as they are stored, saving time and operational complexity.
Athena also integrates seamlessly with AWS Glue Data Catalog, which allows users to store and manage metadata about datasets in S3. This makes it easier to discover and query datasets consistently across the organization. Users can create tables in Athena using the Glue Data Catalog, allowing for consistent schema definitions and simplifying repeated queries on structured or semi-structured log data.
Option B, Amazon EMR, is a managed Hadoop framework that allows large-scale distributed data processing. While EMR can process vast amounts of data and perform complex transformations, it requires managing clusters, writing scripts in frameworks such as Spark or Hive, and handling scaling considerations. For the requirement of querying data using SQL without creating ETL pipelines, EMR introduces unnecessary complexity and overhead.
Option C, Amazon Redshift, is a fully managed data warehouse suitable for complex analytics and structured data storage. However, Redshift requires loading data from S3 into Redshift tables before queries can be executed. This adds an ETL step, which the company specifically wants to avoid. While Redshift is powerful for large-scale analytics with frequent queries, it is not ideal for ad hoc querying of raw log data directly in S3.
Option D, AWS Glue, is an ETL service used for extracting, transforming, and loading data between sources. Although Glue can catalog data and transform it for analytics, it does not directly support querying data with SQL without creating ETL workflows. Glue is more suited for preparing data for analytics rather than performing ad hoc SQL queries on raw datasets.
Amazon Athena allows the company to query and analyze large volumes of log data directly in S3 using standard SQL, without the need to create ETL pipelines or manage infrastructure. Its serverless nature, schema-on-read capability, and integration with the Glue Data Catalog make it the most efficient and cost-effective solution for interactive analysis of log data.
Question 54:
A company runs multiple ECS Fargate microservices that require access to sensitive configuration parameters and credentials. Each service must access only authorized secrets with automatic rotation and encryption. Which AWS service should be used?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS
Answer:
A) AWS Secrets Manager
Explanation:
AWS Secrets Manager is designed to securely store, manage, and rotate sensitive information. Secrets are encrypted at rest using AWS KMS and can be automatically rotated to enhance security. Fine-grained IAM policies restrict access so each microservice can retrieve only its authorized secrets.
Secrets Manager allows ECS Fargate tasks to access secrets programmatically, eliminating the need for hard-coded credentials. It also integrates with CloudTrail for auditing access and rotation events, providing operational visibility and compliance.
Option B, RDS Parameter Groups, only manage database engine parameters. Option C, EC2 Metadata, is unavailable in Fargate and not designed for multi-service secret management. Option D, EFS, is a file system and does not provide encrypted, role-based secret management.
Secrets Manager aligns with SAA-C03 best practices for containerized microservices requiring secure, auditable, and automated secret management.
Question 55:
A company needs to ingest millions of IoT telemetry events per second in real time, with multiple applications processing the same data simultaneously. Which AWS service should be used?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ
Answer:
A) Amazon Kinesis Data Streams
Explanation:
Amazon Kinesis Data Streams is built for high-throughput real-time ingestion of streaming data. Data is partitioned into shards for parallel processing, allowing multiple consumer applications to read the same data independently. Shards can be scaled to meet increased throughput requirements.
Enhanced fan-out enables low-latency data consumption by multiple consumers without impacting each other’s performance. Data is replicated across multiple Availability Zones, ensuring durability. Integration with AWS Lambda allows serverless, event-driven processing.
Option B, SQS, is designed for decoupling messages but does not support multiple consumers reading the same message efficiently. Option C, SNS, is a pub/sub system but lacks message replay and high-throughput streaming optimizations. Option D, Amazon MQ, is a managed broker for traditional messaging, unsuitable for millions of events per second.
Kinesis Data Streams meets the requirements for real-time ingestion, durability, scalability, and multi-consumer access, making it the best solution for IoT telemetry processing in SAA-C03 scenarios.
Question 56:
A company wants to deliver static web content stored in Amazon S3 globally with low latency and prevent users from accessing the content directly from the S3 bucket. Which solution best meets these requirements?
A) Amazon CloudFront with Origin Access Control
B) Amazon SNS with HTTPS
C) Amazon Global Accelerator with EC2 backend
D) Public S3 bucket with HTTPS
Answer:
A) Amazon CloudFront with Origin Access Control
Explanation:
Amazon CloudFront is a globally distributed content delivery network (CDN) designed to deliver web content, including static files such as HTML, CSS, JavaScript, images, and videos, with minimal latency and high performance. CloudFront operates by caching content at edge locations strategically placed around the world. When a user requests content, CloudFront serves the request from the nearest edge location, reducing latency and improving the end-user experience compared to retrieving content directly from a central Amazon S3 bucket or an origin server.
A key requirement for this scenario is preventing direct access to the S3 bucket. Origin Access Control (OAC) in CloudFront allows you to achieve this by creating a secure relationship between the CloudFront distribution and the S3 bucket. With OAC enabled, S3 only accepts requests from the CloudFront distribution, ensuring that users cannot bypass the CDN and access the bucket directly. This is critical for securing sensitive content and controlling how it is delivered, which aligns with AWS security best practices.
CloudFront also supports HTTPS, ensuring secure transport of content over the network. HTTPS encrypts data in transit, protecting it from man-in-the-middle attacks and maintaining the confidentiality and integrity of user requests. Additionally, CloudFront integrates with AWS Web Application Firewall (WAF) to provide protection against common web exploits and attacks such as SQL injection or cross-site scripting (XSS). Caching policies in CloudFront further optimize performance by controlling how long content is stored at edge locations and when it is refreshed from the origin.
Option B, Amazon SNS, is a pub/sub messaging service intended for event notifications and cannot be used to serve static content. Option C, Amazon Global Accelerator, improves network-level performance and directs traffic to optimal endpoints, but it does not provide caching of S3 content, meaning it cannot reduce latency for static files in the same way CloudFront can. Option D, making the S3 bucket public, exposes content to unauthorized users and violates AWS security best practices, leaving sensitive content vulnerable.
In practice, using CloudFront with OAC ensures not only fast, globally distributed delivery but also secures the S3 bucket from direct public access. This pattern aligns with the AWS Well-Architected Framework pillars, including performance efficiency, reliability, and security. From an SAA-C03 exam perspective, understanding how CloudFront, OAC, and S3 interact is critical for designing secure, high-performing content delivery architectures that meet both compliance and performance requirements.
Question 57:
A company wants to deploy a multi-tier web application with EC2, RDS, and a caching layer. They need the database to be highly available, with automatic failover in case of a primary instance failure. Which configuration is most appropriate?
A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache for caching
B) Single RDS instance with periodic snapshots and ElastiCache
C) RDS read replicas only for failover
D) EC2-hosted database with custom replication
Answer:
A) Amazon RDS Multi-AZ deployment with Amazon ElastiCache for caching
Explanation:
In multi-tier web applications, it is critical to ensure that each layer—presentation, application, and data—is resilient and performs optimally. The database layer often represents a single point of failure, so high availability is a key design requirement. Amazon RDS Multi-AZ deployment addresses this by synchronously replicating the primary database instance to a standby instance in another Availability Zone (AZ). If the primary instance fails due to hardware issues, software problems, or an AZ outage, failover occurs automatically. The application experiences minimal downtime because RDS handles the promotion of the standby instance to primary, without manual intervention.
In addition to high availability, performance is often improved with a caching layer. Amazon ElastiCache, a fully managed in-memory caching service supporting Redis or Memcached, reduces database load by caching frequently accessed data. This improves response times and decreases latency for end users. By combining RDS Multi-AZ deployments with ElastiCache, the architecture ensures both high availability and high performance, critical for multi-tier applications with fluctuating workloads.
Option B relies on manual snapshot recovery, which introduces longer downtime during failures and cannot provide real-time failover. Option C, RDS read replicas, are primarily used for scaling read-heavy workloads and cannot automatically replace a failed primary instance, making them insufficient for high availability. Option D, hosting a database on EC2 with custom replication, introduces complexity in managing replication, backups, failover procedures, and patching, which increases operational overhead and the risk of misconfiguration.
From a best-practices standpoint, this solution aligns with the AWS Well-Architected Framework pillars of reliability, performance efficiency, and operational excellence. Multi-AZ RDS ensures fault tolerance, ElastiCache optimizes latency and database load, and the overall architecture reduces operational complexity. For the SAA-C03 exam, scenarios testing high availability and caching integration often expect candidates to identify RDS Multi-AZ plus caching as the standard design pattern for resilient multi-tier applications.
Question 58:
A company processes large-scale IoT telemetry data and wants multiple applications to consume the same data simultaneously. They require durability, low latency, and scalability. Which AWS service is most appropriate?
A) Amazon Kinesis Data Streams
B) Amazon SQS Standard Queue
C) Amazon SNS
D) Amazon MQ
Answer:
A) Amazon Kinesis Data Streams
Explanation:
Amazon Kinesis Data Streams is designed for high-throughput, real-time ingestion and processing of streaming data. In IoT scenarios, millions of events per second can be generated from sensors, devices, and applications. Kinesis Data Streams divides data into shards, which can be scaled horizontally to handle increased throughput. Each shard can accept a certain number of records per second, allowing applications to process large volumes efficiently without congestion or bottlenecks.
A key advantage of Kinesis is fan-out support. Multiple consumer applications can read the same stream concurrently without interfering with each other. Enhanced fan-out allows consumers to receive data with sub-millisecond latency, which is essential for time-sensitive IoT applications. The service also replicates data across multiple Availability Zones, providing durability and reliability. This guarantees that even if one AZ fails, the data stream remains available and intact.
Option B, SQS, is a message queue service suitable for point-to-point communication. While SQS ensures delivery, it is not optimized for multiple consumers reading the same message simultaneously. Option C, SNS, is a pub/sub notification service, but it does not provide message replay or retention for high-throughput streaming; once a message is delivered, it cannot be retrieved again. Option D, Amazon MQ, is a managed message broker for traditional applications, but it is not optimized for millions of messages per second and incurs higher latency compared to Kinesis.
Integrating Kinesis with AWS Lambda, Amazon S3, and analytics services enables real-time processing, transformation, and storage of streaming data in a serverless architecture. Security features, such as encryption at rest with KMS and IAM-based access control, provide secure management of sensitive telemetry data. For SAA-C03 exam purposes, understanding the differences between Kinesis, SQS, SNS, and MQ is essential, as the exam often tests the ability to select the right streaming solution based on throughput, fan-out requirements, and durability.
Kinesis Data Streams satisfies the critical requirements: multiple consumers can access the same data, high durability is ensured across AZs, low latency is maintained, and horizontal scalability accommodates growing IoT workloads. This makes it the most appropriate choice for real-time IoT data processing.
Question 59:
A company runs multiple ECS Fargate microservices that require secure access to sensitive credentials and API keys. Secrets must be encrypted, rotated automatically, and restricted per service. Which solution should be used?
A) AWS Secrets Manager
B) Amazon RDS Parameter Groups
C) EC2 Instance Metadata
D) Amazon EFS
Answer:
A) AWS Secrets Manager
Explanation:
AWS Secrets Manager is a fully managed service that securely stores and manages secrets such as API keys, passwords, and certificates. It uses encryption with AWS Key Management Service (KMS) to protect secrets at rest and ensures that only authorized services or users can access them via fine-grained IAM policies. For ECS Fargate tasks, Secrets Manager can integrate with task roles, allowing each microservice to access only the secrets it requires. This eliminates hard-coded secrets in application code, reducing security risks.
Secrets Manager also supports automatic rotation, which is essential for maintaining security compliance and reducing operational overhead. Rotation can be configured with custom Lambda functions to handle application-specific credential update logic without manual intervention. CloudTrail integration enables auditing of all secret access and rotation events, providing transparency and traceability for security operations.
Option B, RDS Parameter Groups, is limited to managing database configuration parameters and cannot store general secrets. Option C, EC2 instance metadata, is not available in Fargate since Fargate tasks do not run on traditional EC2 instances with metadata endpoints. Option D, EFS, is a file storage solution and does not provide built-in encryption, rotation, or role-based secret management.
Using Secrets Manager aligns with AWS security best practices, reduces operational risk, and ensures compliance with standards for containerized and serverless applications. On the SAA-C03 exam, scenarios often test knowledge of secure secret management for microservices, serverless applications, and multi-tenant environments, making Secrets Manager the recommended solution.
Question 60:
A company stores sensitive files in S3 and requires fine-grained access control, encryption at rest, and encryption in transit. Which combination meets these requirements?
A) S3 bucket policies, IAM roles, and SSE-KMS
B) S3 ACLs with SSE-S3 only
C) Security groups and client-side encryption
D) Public bucket with HTTPS
Answer:
A) S3 bucket policies, IAM roles, and SSE-KMS
Explanation:
Securing sensitive files in Amazon S3 requires a combination of access control, encryption, and auditing. Bucket policies enable fine-grained permissions, allowing administrators to define exactly which IAM users, roles, or services can access specific objects. IAM roles further restrict access to authorized entities and integrate seamlessly with AWS services, ensuring that applications and users follow the principle of least privilege.
Server-Side Encryption with AWS Key Management Service (SSE-KMS) encrypts data at rest using customer-managed keys. This provides an additional layer of security, including centralized key management, detailed audit logs of key usage, and compliance with regulatory standards. Using HTTPS for data in transit ensures that the data is encrypted while moving over the network, preventing interception or tampering.
Option B, using ACLs with SSE-S3, is less flexible and does not provide KMS-based auditing and control. Option C, relying on security groups and client-side encryption, does not provide granular S3-level access control and adds operational complexity for managing keys and encrypting files at the client. Option D, making the bucket public, violates security best practices and exposes sensitive data to unauthorized users.
This solution aligns with the AWS Well-Architected Framework security pillar, emphasizing confidentiality, integrity, and access control. In SAA-C03 exam scenarios, understanding the combination of bucket policies, IAM roles, SSE-KMS, and HTTPS is crucial for demonstrating knowledge of secure, compliant storage architectures. This approach ensures that sensitive data is protected at all stages while maintaining operational simplicity and auditability.